Now is the time for the telecommunications industry to devise a strategy for modernization of the data management environment in ways that streamline the end-to-end processes for ingesting, transforming, loading, reporting, delivering, and analyzing data. This Checklist Report describes a vision for the future that includes using an architecture that leverages a hybrid environment combining traditional enterprise data warehouse (EDW) techniques with the ability to deploy augmentation tactics on big data environments, such as those built using the Hadoop ecosystem.
Forward-looking businesses need discovery-oriented analytics, but discovery analytics tends to work best with large volumes of raw source data. The data lake enables analytics with big data and other diverse sources. This TDWI Checklist Report discusses many of the emerging best practices for data lakes.
User organizations facing new and future requirements for big data, analytics, and real-time operation need to start planning today for the data warehouse of the future. This Checklist Report drills into seven key recommendations for solution design, listing and discussing many of the new vendor and open source product types, functionality, and user best practices that will be common in the near future, along with the business case and technology strengths of each.
This Checklist Report drills into some of the emerging design patterns and platforms for data that modern data-driven organizations are embracing. The goal of the report is to accelerate users’ understanding of new design patterns and data platforms so they can choose and use the ones that best support the new data-driven goals of their organizations.
This checklist defines data security and data-centric security and discusses best practices and enabling technologies to help make data more secure.
This checklist will help you and your team plan and launch successful data lake projects with your legacy data sources. It reviews the critical success factors for these projects as well as the risks and issues to mitigate.
A data lake ingests data in its raw, original state, straight from data sources, with little or no cleansing, standardization, remodeling, or transformation. These and other data management best practices can then be applied flexibly as diverse use cases demand.
Most data lakes are built atop Hadoop, which enables a data lake to capture, process, and repurpose a wide range of data types and structures with linear scalability and high availability.
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